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Record W4313042633 · doi:10.4236/ojrad.2022.124021

Tuberculosis: A Head to Toe Radiological Review

2022· article· en· W4313042633 on OpenAlexaff
Alexandre Semionov, Kiana Lebel, Ange Diouf, Joséphine Pressacco

Bibliographic record

VenueOpen Journal of Radiology · 2022
Typearticle
Languageen
FieldMedicine
TopicInfectious Diseases and Tuberculosis
Canadian institutionsMontreal Heart InstituteUniversité de MontréalUniversité de SherbrookeMcGill University Health Centre
Fundersnot available
KeywordsMedicineTuberculosisRadiological weaponRadiological imagingMagnetic resonance imagingRadiologyRadiographyModalitiesDiseaseConventional radiographyPathology

Abstract

fetched live from OpenAlex

Tuberculosis (TB) results from infection by Mycobacterium tuberculosis and can involve any organ or tissue. Early diagnosis of TB is essential for timely initiation of therapy in order to decrease transmission rate and avoid severe morbidity associated with delayed treatment. Although conventional radiography remains the most common initial imaging modality in diagnosis of pulmonary TB, computer tomography (CT) and magnetic resonance imaging (MRI) are modalities of choice with regards to diagnosis of extra-pulmonary TB. The purpose of this paper is to provide a concise review of various imaging manifestations of TB in various organ systems, in order to promote radiologists’ and clinicians’ familiarity with common radiological findings of this important disease.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.147
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.031
GPT teacher head0.352
Teacher spread0.321 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2022
Admission routes1
Has abstractyes

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